View source on GitHub |
Bin values into discrete intervals.
tfp.substrates.jax.stats.find_bins(
x,
edges,
extend_lower_interval=False,
extend_upper_interval=False,
dtype=None,
name=None
)
Given edges = [c0, ..., cK]
, defining intervals
I_0 = [c0, c1)
, I_1 = [c1, c2)
, ..., I_{K-1} = [c_{K-1}, cK]
,
This function returns bins
, such that x[i]
lies within I_{bins[i]}
.
Returns | |
---|---|
bins
|
Tensor with same shape as x and dtype .
Has whole number values. bins[i] = k means the x[i] falls into the
kth bin, ie, edges[bins[i]] <= x[i] < edges[bins[i] + 1] .
|
Raises | |
---|---|
ValueError
|
If edges.shape[0] is determined to be less than 2.
|
Examples
Cut a 1-D
array
x = [0., 5., 6., 10., 20.]
edges = [0., 5., 10.]
tfp.stats.find_bins(x, edges)
==> [0., 1., 1., 1., np.nan]
Cut x
into its deciles
x = tf.random.stateless_uniform(shape=(100, 200))
decile_edges = tfp.stats.quantiles(x, num_quantiles=10)
bins = tfp.stats.find_bins(x, edges=decile_edges)
bins.shape
==> (100, 200)
tf.reduce_mean(bins == 0.)
==> approximately 0.1
tf.reduce_mean(bins == 1.)
==> approximately 0.1